Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
  • G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
  • H04N13/20—Image signal generators
  • H04N13/204—Image signal generators using stereoscopic image cameras
  • H04N13/207—Image signal generators using stereoscopic image cameras using a single 2D image sensor
  • H04N13/221—Image signal generators using stereoscopic image cameras using a single 2D image sensor using the relative movement between cameras and objects

Definitions

• This invention relates in general to imaging and, more particularly, to a method and apparatus of using a moving camera or fixed sensor array for 3-dimensional scanning of an object.
• 3D scanners have become available. 3D scanners of this type project a laser pattern on an object and determine the location of points on the object using triangulation techniques. These systems can be extremely complicated and, therefore, costly. Further, laser systems are limited to a monochromatic light source, limiting their usefulness. Still further, some materials may be opaque or transparent at the frequency of the laser, further limiting their usefulness.
• Another object of the present invention is to provide an accurate object modeling method and apparatus that is multi-spectrum, able to provide models of night scenes and able to provide color models.
• It is another object of the present invention is to provide an accurate object modeling method and apparatus requiring a minimum of human intervention and setup.
• a discrete linear space sampling method for generating digital 3D models comprises acquiring a plurality of digital images of a subject from a respective plurality of image sensor positions near to an image sensor location, identifying candidate 3d-spels, wherein each 3d-spel is an image pixel corresponding to a common point on the subject, rejecting candidate 3d-spels based on a differential analysis of the candidate 3d-spels, wherein the remaining 3d-spels form a set of accepted 3d-spels, and calculating 3D coordinates for each accepted 3d-spel.
• the resulting 3D coordinates form a point-cloud of the subject.
• a system for generating digital 3D models comprises a means for acquiring a plurality of digital images of a subject from a plurality of image sensor positions near the image sensor location, a means for identifying candidate 3d-spels, a means for rejecting candidate 3d-spels based on a differential analysis of the candidate 3d-spels, a means for calculating 3D coordinates for each remaining 3d-spel, thereby forming a point-cloud of the subject.
• Fig. 1 is a flowchart illustrating a preferred method of 3D modeling in accordance with aspects of the present invention.
• Fig. 2 is a diagram illustrating parallax from two observer locations.
• Fig. 3 shows a common set of 3 points from two image sensor locations.
• Fig. 4 shows two coordinate systems prior to a sequence of operations to align the coordinate systems.
• Fig. 5 illustrates a method of marching cubes suitable for practicing aspects of the present invention.
• Fig. 6 shows a cube having one vertex inside the object being modeled.
• Fig. 7 shows a cube having three vertices inside the object being modeled.
• Fig. 8 is a front view of the cube in Fig. 7.
• Fig. 9 shows a cube having four vertices inside the object being modeled.
• Fig. 10 shows the 15 cases of cubes according to aspects of the present invention.
• Fig. 11 is a schematic representation of an image sensor mounted on a slider according to one embodiment suitable for practicing aspects of the present invention.
• Fig. 12 illustrates an array of horizontal cross sections of color images obtained and processed in accordance with an exemplary embodiment of the present invention.
• Fig. 13 shows a near point and a distant point of an object being modeled and the corresponding horizontal cross section images as per FIG. 12.
• Fig. 14 shows a more complex horizontal cross section than shown in Fig.
• Fig. 15 shows a row of pixels from a center image of color images obtained and processed in accordance with an exemplary embodiment of the present invention.
• Fig. 16 shows a color variance boundary for the cross section of Fig. 15.
• Fig. 17 illustrates an array of horizontal cross sections of gray scale images obtained and processed in accordance with an exemplary embodiment of the present invention.
• Fig. 18 is an exemplary horizontal cross section from Fig. 17.
• Fig. 19 shows a point cloud map of an exemplary object in accordance with aspects of the present invention.
• Fig. 20 shows a triangle mesh of an exemplary object in accordance with aspects of the present invention.
• Fig. 21 shows a photo-realistic image of an exemplary object in accordance with aspects of the present invention.
• Fig. 22 shows a single-sensor imager having a camera movable on a track suitable for one embodiment of the present invention.
• Fig. 23 shows a multi-sensor imager suitable for another embodiment of the present invention.
• Fig. 24 shows a pan-and-tilt imager suitable for yet another embodiment of the present invention.
• Fig. 25 shows an alternate imager having an array of fixed sensors suitable for yet another embodiment of the present invention.
• Fig. 26 shows a viewing and imaging tool window according to one embodiment suitable for practicing aspects of the present invention.
• DLSS Discrete Linear Space Sampling
• Suitable object or scene data is acquired (12) by using image sensors to collect digital images of the object or scene.
• 3D coordinates are generated (14) from digital images by means of the DLSS system and process.
• DLSS system and process generates a stereo- vision zone by analyzing data from a sensor traveling along a horizontal base.
• the DLSS system registers points and automatically filters noisy or superfluous data (16) optionally with no input from the user.
• the DLSS system automatically builds a triangular mesh (18) from the data it collects.
• the DLSS system automatically pastes texture (20) from the collected digital images onto the 3D triangular mesh model, thereby producing a photo-realistic 3D model of the object or scene.
• DLSS Theory provides the mathematical foundation for DLSS.
• Digital Data Capture describes how digital image sensors are used to capture the scene or object being modeled.
• 3d-spels describes how the digital images are used as input so that a cloud of three-dimensional points can be generated, h DLSS terminology, these points are called 3d-s ⁇ els (three dimensional spatial elements). These 3d-spels form a point cloud model of the object or space being modeled.
• Point Registration describes how the 3d-spels from two or more image sensor locations are correlated and registered.
• Triangulation and Mesh Generation describes how, after the 3d-spels have been generated, a triangle mesh of the object or space is generated. It is shown how the 3d- spels are used as vertices of triangles that form a triangle mesh of the object or scene being modeled.
• Texture Mapping describes how the texture from the original set of images is identified and "pasted" onto the triangles in the triangle mesh, hi this last step of 3D model building, the process starts with the mesh and ends with a photo-realistic, spatially accurate model of the 3D object or scene.
• 3D spatial coordinates are calculated using digital images
• the 3d-spels are arranged to form a triangle mesh of the space.
• Parallax is the change in the apparent position of an obj ect due to a shift in the position of an observer. Parallax has been widely used to find the distances to stars and to find large distances on earth that are too difficult to measure directly.
• the "obj ect" is a point in space.
• the two positions of the observer are simply two image sensor positions a known distance apart.
• the image sensor optionally comprises a digital camera including a CCD, lens, etc. as is known in the art.
• the image sensor comprises a fixed linear array of sensors. While the various embodiments of the present invention are discussed with respect to movable cameras, it is to be appreciated that fixed arrays of sensors are also suitably adapted to, and included in the scope of, the present invention. This arrangement is shown in Figure 2.
• F (22) is the focal length of the lens and B (24) is the distance between the centers of the two image sensors at the two image sensor positions.
• the actual focal points of the image sensor lenses are labeled Focal Point 1 (26) and Focal Point 2 (28), respectively.
• C2 (34) is defined similarly for image sensor 2 with respect to its corresponding detected pixel 36.
• h DLSS, CI (30) and C2 (34) are obtained by multiplying the pixel width times the number or pixels the image point is offset from the center of the CCD of the respective image sensor.
• D (38) is the distance (z coordinate) to the point (40) in space.
• Equation 1.1.1a Equation 1.1.1a
• the different image sensor locations are preferably obtained in either one of two ways: 1. (Case A) Applying known transformations to the image sensor position.
• a transformation as used herein, means one image sensor location is obtained from another by a known translation and/or a known rotation; or,
• the 3d-spels generated from different image sensor locations should have accurate 3D coordinates, but these coordinates are with respect to different origins. Sets of 3d-spels are, therefore, combined into a single set with a common origin. If the 3d-spels are generated as in Case A above, 3d-spels are transformed to a common coordinate system by applying the inverse transformation. For Case B above, 3d-spels are registered to a common origin by the use of Euler angles.
• Linear Transformations are used to map one vector space into another.
• Linear transformations satisfy two properties:
• T( ⁇ b) ⁇ T (b) for any scalar ⁇ and any vector b.
• An affine transformation is one of the three basic operations of rotation, scaling, and translation. If these operations are viewed as mapping 3-space (3 dimensional space) to 3-space, there is no known matrix representation for translation since it fails the second property of LTs.
• the affine transformations have matrix representations
• the matrix for a sequence of transformations is obtained by multiplying the individual matrices together.
• the inverse transformation is applied to the 3d-spels from B. This means for each 3d-spel S collected at location B, the 3d-spel S can be registered to a 3d-spel S' at image sensor location A for B by
• DLSS employs another method of registering points. This method is based on the following fact: performing one translation and 3 rotations can align any two coordinate systems.
• the two coordinate systems are established by locating the same set of 3 points in two separate image sensor locations. With reference to Figure 3, at each image sensor position, the three points 44, 46, 48 are located and given 3D coordinates as previously described.
• Two vectors 50, 52 are formed from the 3 points 44-48 and the normal vector 54, denoted by ⁇ , to the plane as calculated by taking the cross product of VI with N2.
• the direction represented by ⁇ becomes the positive y-axis for the new coordinate system.
• One of the vectors, NI or N2 is selected as the direction of the positive x-axis. These vectors are perpendicular to ⁇ , but not necessarily to each other.
• the positive z-axis is determined by crossing ⁇ with the vector selected for the x-axis.
• the z' and z axes are made to align by rotating points about the x-axis so that the z' axis is in the x-z plane.
• a tangent plane is established at each 3D point on the surface of the object. These tangent planes are used in triangulation and, since the texture will be pasted on the visible surface, the normal vector to each plane is assigned such that it points away from the visible surface. This prevents the texture from being pasted on the inside surface of the object being modeled.
• the least squares plane is constructed. This plane serves as the tangent plane. Curve fitting is done on all points in a neighborhood of the point (x,y,z). The size of the neighborhoods can vary to make the number of planes larger or smaller.
• the coverage matrix, C is created. This matrix is defined to be the sum of all of the inner products of vectors of the form (v-0) with themselves where: a. v is in the neighborhood of (x,y,z); and b. 0 is the center of mass of the points in the neighborhood of (x,y,z).
• a minimal spanning tree is built. For a set of edges and vertices such as those collected by DLSS, a minimal spanning tree is simply a graph in which the sum of the lengths of the edges is minimal and that contains all the vertices.
• a normal vector is expected to be oriented similarly to that for neighboring tangent planes. For example, a cluster of 10 planes would not generally be expected to have 9 normal vectors pointing in one direction while the 10th points in the opposite direction. Based on threshold values, a normal vector is negated based on its neighbors.
• a surface-fitting algorithm known as Marching Cubes, is implemented for visualizing the surface of a 3D object.
• the Marching Cubes algorithm was developed in 1987 by Lorensen and Cline. ⁇ W.E. Lorensen and H.E. Cline. Marching cubes: A high resolution 3D surface reconstruction algorithm. Computer Graphics, 21(4): 163 -169, July 1987. ⁇ It uses a specified threshold value to determine the surface to render. The basic idea is based on this principle: "If a point inside the desired volume has a neighboring point outside the volume, the iso-surface must be between these points.”
• Figure 5 illustrates the process for the two dimensional case.
• the figure on the left 80 is scanned to determine which rectangles 82 are intersected; in two dimensions a rectangle is a cube. If an intersection occurs, the midpoint of the edge where the intersection occurs is marked. By connecting consecutive midpoints, an approximation of the original surface is made, hi the case illustrated, the approximation 84 is relatively crude, but it can be improved by increasing the resolution of rectangles.
• the three-dimensional case works analogously but, as expected, it is more complicated. It has been observed that cubes rather than squares approximate the boundary of the surface. Note that it is important to know whether a point is inside or outside the surface. This is accomplished by using the signed distance previously described. Recall that the signed distance to the surface is the distance from the point to the surface with a negative sign affixed if the point is inside the surface and a positive sign affixed if the point is outside the surface.
• FIG. 7 Another of the 16 cases is shown in Figure 7. hi this case, 3 of the vertices 92, 94, 96 are found to lie inside the surface being modeled while the remaining 5 vertices fall outside, hi this case, 3 triangles 98, 100, 102 are added to the set of vertices. The first 98 is obtained by adding the vertex that is not on the same face as the remaining two by the method described above.
• the first added triangle 100 has, as its base, the edge between the two vertices known to lie inside the surface.
• the third vertex 104 is chosen as the midpoint of either of the edges on the opposite face.
• the second added triangle 102 is chosen with its base as the midpoints 104, 106 of the two edges on the opposite face and its vertex 96 as one of the 2 vertices known to lie in the cube. This essentially creates a plane that passes through the two vertices 94, 96 and the midpoints 104, 106 of the opposite face.
• a side view of the process is illustrated in Figure 7, while Figure 8 illustrates a front view of the same process.
• Texture mapping or pattern mapping is the process by which a texture or pattern in a plane is mapped to a 3D object.
• the objective is to map the texture or pattern from a subset of a 2D bitmap to the triangle that represents that subset in 3-space.
• the 3D modeling process begins with pixel coordinates from a set of 2D images. These pixels are passed through a process that assigns them 3D coordinates.
• a 3d-spel is connected to the pixel coordinates from which it originated.
• the first step in a preferred embodiment of the DLSS process is digital data capture.
• data is captured from several image sensor positions, or several image sensors, and combined by the previously described methods.
• the image sensor is mounted on a slider 130 of length L as shown in Figure 11.
• the image sensor is moved from the left edge of the slider to the right edge in discrete steps 132, all of length ⁇ L.
• a digital image is taken of the scene or object 134 being modeled.
• the base is of length L
• N+l images are collected, each image sensor location being displaced ⁇ L units from the previous location.
• the image sensor moves on a slider (the base) and stops at points that are L/N ( ⁇ L) apart to collect a digital image.
• the image sensor is parallel to the direction in which the image sensor slides.
• Generating 3d-spels involves 2 steps.
• the first step is to find the same point in two of the images that were collected in the Data Capture Step. This means that, firstly, that pairs of pixels are found, one from image 1 and one from image n, that correspond to the same point in space. Such points are referred to as candidate 3d-spels.
• the candidate 3d-spels are accepted or rejected based on criteria described below.
• the second step is to calculate the coordinates for an accepted candidate 3d-spel by the methods previously described in the section on DLSS theory. As indicated in that section, calculating the (x,y,z) coordinates is straightforward provided a proper point is located. However, care is taken to ensure that the pixels in image 1 and image n do in fact represent the same point in space to ensure accurate 3D models.
• DLSS embodiments optionally use one of two methods to generate candidate 3d-spels, and to accept or rej ect them as actual 3d-spels.
• One method is based on color differential in the center image. This method is called Color Differential Analysis (CD A).
• CD A Color Differential Analysis
• GDA Gray-Scale Differential Analysis
• the first step in CDA is to form a three dimensional array from the sequence of images 140 previously generated as shown in Figure 12. If the array is called E, then ⁇ (m,n,N) represents the color or intensity of the pixel in row m and column n of the Nth image.
• Factors used for candidate 3D-spel location by color differential and DLSS methods are the cross sections 142 of the array of images. These cross sections 142 are simply y-slices of the array E that represent the points in the images located at a fixed y- value in space. It is now apparent why the assumption was made that there are no changes in y- values for an obj ect as the image sensor moves along the slider during the data capture step. Since analysis is done on horizontal cross sections, it follows that the y-values should be consistent from image 0 to image ⁇ .
• a first scene is shown 144 (left) that contains a near point 146 and a more distant point 148.
• the positions of the near and distant points 146, 148 are shown in each of the images 0,1,2,..., ⁇ .
• This figure maybe thought of as the array E described above, h the right scene 152, a horizontal cross-section of E as viewed from the top is shown. Since the near point is closer to the image sensor, it moves farther than the distant point as the image sensor moves from left to right.
• a typical cross section 154 may look more like that shown in Figure 14. From this diagram, it is seen that it may be difficult to match pixels in the first image with those in the last image. For example, any of the near pixels in the back image 156 (image N) are candidates to match any of the distant pixels in the front image 158 (image 0). The same may be said of intermediate pixels as well.
• CDA matches pixels by first looking for color differential in the aforementioned center image.
• color differential what is meant is an abrupt change in color moving across a set of pixels for a fixed y- value, hi Figure 15, the arrow 160 indicates the pixels corresponding to the center image in a fixed horizontal cross section.
• the circles 162 identify points where significant color differential is found as pixels are tested from left to right. These become the candidate points for 3d-spels.
• CDA tests a family of lines 164 that pass through the points of color differential and that pass from the first image 166 to the last 168. Such a family 170 is illustrated in Figure 16 with dashed lines.
• the dispersion or color variance of the pixel line is calculated.
• this is simply the variation in the Red, Green, and Blue components of the pixels along this line. From the diagram, it is seen that the minimum variance will be on the line that represents a definite boundary 172 between objects of different color. In fact, if noise were not present (due to aliasing, inexact lighting or instrument error for example), the variance would be zero along such a line. For points not on the boundary line 172, the color variance will be non-zero.
• the line with the minimum color variance is tested. If the variance along the line with minimum color variance exceeds a pre-set threshold, the point is rejected and analysis proceeds to the next point. If this variance is below a certain threshold, the point is accepted as a 3d-spel. It is easily seen that, should a point be selected, the needed pixel offsets may be found so that the (x,y,z) coordinates of the point may be calculated as previously described.
• DLSS methods In addition to the CDA analysis described above, DLSS methods also identify candidate 3dspels by a second method that performs Gray Scale Differential Analysis on gray scale images. This method allows considerable performance gains over CDA by using simpler methods for locating candidate 3d-spels and for searching.
• the image capture process takes grayscale images, rather than the color images used by CDA, as the image sensor slides along horizontally. These pictures are first passed through an edge detection algorithm. Edge detection takes the place of the analysis of rows of pixels in the center image. The result of the edge detection process is a second series of grayscale pictures. The intensity value in these images indicates the degree of ' "strength" or "hardness" of the edge, 0 for no edge, 255 for a large discontinuity for example.
• edge-detected images are arranged in a 3 dimensional array, just as the color images were in CDA, as shown in Figure 17.
• horizontal cross sections 174 of this array are analyzed.
• each cross section 174 is comprised of the same vertical row of edge strengths 176 from each of the images.
• edges 176 are found that can be followed as they move through the images taken by adjacent image sensors.
• the process of finding all lines in all cross images is a highly computationally intensive task.
• the search algorithm tries to minimize the search time and reduce the search space.
• the primary means of minimizing line searching is to look in the bottom row 178 for a strong edge, and when one is found, try to find an edge in the top row 180 that matches it. If such a match is found, a candidate 3d-spel has been located and an attempt is made to follow the edge through the other images.
• the degree of fit is measured by finding all pixels on the line 176 drawn between strong edges identified on the top and bottom images. If all pixels on the line exceed a certain threshold, and if they are all of roughly the same strength, then it is concluded that the line represents a point in 3-space. The (x,y,z) location of the point can then be determined from the image as described previously.
• 3d-spels are calculated at all of the image sensor positions, They are merged into a single data set of 3d-spels with a common origin. This allows a point cloud generation of the entire space or object.
• one image sensor position may be obtained from another by:
• Case A the inverse transformation is applied for each transformation in the known series. For example, suppose image sensor position B is obtained from an image sensor location by translating points by +10 units in the y-direction and rotating by 45 degrees about the y-axis. To register the points from image sensor B to the origin for the image sensor location, the transformations is applied that rotates by -45 degrees about the y-axis and translates by -10 in the y-direction.
• DLSS methods use routines that locate the same three points in each of camera A and camera B.
• the same set of 3 points is located from each of the camera positions.
• this step is separate from actual data capture from a given camera location.
• DLSS embodiments proceed as follows: a. Use the 3 points to construct a coordinate system for each camera; and b. Combine the data by converting one coordinate system to the other.
• the next step in the DLSS preferred embodiment is to build a mesh, in particular a triangle mesh, from the set of 3d-spels gathered as described above. After the points are loaded in an appropriate data structure, they are filtered.
• the filtering step is preferable.
• the 3d-spel generating program generates a large number of points, often far too many to allow the triangulation procedure to work efficiently, hi other teclmologies, such as laser technology, the number and approximate location of 3D points are controllable by the user.
• all 3d-spels identified by CDA or GDA are included, hi areas of high contrast or in areas replete with edges, many extra or superfluous points may be generated.
• the filtering algorithm seeks a single, representative point to replace points that are "too close” together.
• “too close” it is meant that the points all lie with a sphere of radius R, where R is an input parameter to the triangulation subsystem.
• R is an input parameter to the triangulation subsystem.
• R increases, more and more points are removed.
• R decreases, the number of points retained is increased.
• the size of R affects the final 3D resolution of the surface.
• 3D-spels may simply be noise. Due to the lighting, reflections, shadows, or other anomalies, points are generated that are not part of the surface. These points are preferably located and removed so that the surface is modeled correctly. Generally, noisy points are characterized by being "far away from any supporting points", i.e. they are very sparsely distributed within some sphere.
• the construction of the triangle mesh itself is done by the method of Marching Cubes.
• Marching Cubes is a well-documented way of generating a triangle mesh from a set of points.
• the Method of Marching Cubes was developed by Lorensen and Cline in 1987 and has since been expanded to include tetrahedra and other solids.
• the input to this algorithm is the filtered set of points and a signed distance from the object being modeled to each of the points.
• Controllable volume simply means that the length of one edge of the cube is an input parameter to the triangulation subsystem. It is easily seen that as the volumes of the cubes decreases, the surface is modeled more closely.
• each triangle is oriented, i.e., the visible face of the triangle is determined.
• the orientation step is performed since the texture of the surface is pasted on the visible side of the triangle. An incorrect orientation would result in a partially or completely inverted surface.
• the triangulation process is finished, it is possible to display the object or space being modeled as a wire frame or wire mesh 184 as shown in Figure 20.
• the last step in the preferred DLSS method is to map texture to each of the triangles generated in the Triangulation process.
• the input to this step is a.
• the Single Sensor Imager is appropriate for modeling lateral surfaces of an object or objects placed on a rotating table.
• a typical single sensor arrangement 188 is shown in Figure 22.
• the first parameter, R specifies how many views are made of the object 190, or equivalently, how many discrete stops the rotating table 192 makes.
• the object space maybe viewed at 12 positions 194 that are 30 degrees apart or it maybe viewed at 8 positions that are 45 degrees apart.
• the second parameter, N determines how many images (N+l) are taken at each rotation position of the rotating table. This input is the same quantity, N, described in the section for generating 3d-spels.
• the third parameter, B specifies the length of the slider base 196 on which the camera 197 slides. This quantity controls the leftmost and rightmost positions of the image sensor on the slider and tells how far the image sensor moves between the first and the N+lth image.
• a user may elect to project a grid of lines or points onto the object being modeled. This has the effect of introducing artificial edges and/or artificial points of color differential. Using these artificially generated edges, more candidate 3d-spels can be located on the object and, consequently, the model is more accurate.
• the exemplary single image sensor obj ect modeler is suited to providing lateral views of an object. Due to the fixed position of the image sensor, it is possible that the top or bottom of an object will be insufficiently modeled. In the next section, a DLSS setup is described that can model an object from multiple image sensor positions.
• the Multi-Sensor Imager is an extension of the single image sensor modeler.
• each individual image sensor in the multi-sensor imager is simply an instance of a single sensor imager 198.
• the purpose of adding additional image sensors is to capture data from regions such as the top or bottom of the object being modeled.
• FIG. 23 An exemplary two-image sensor setup 200 is shown in Figure 23.
• the second image sensor is used to capture data from the top of the object. It is noted, however, that the methodology is not restricted to two image sensors. As many image sensors may be used as necessary.
• the user To use the multi-sensor system, the user first registers the various image sensor positions, i.e., the user establishes an origin that is common to the data collected from each image sensor. This is done by locating the same three points in each sensor and applying the coordinate transformations previously described.
• the user specifies the parameters R, N, and B described above. These parameters may vary for each image sensor or they may all be assigned common values. As in the single image sensor case, the user may elect to project a grid on the object to introduce artificial edges and points of color differential.
• the 3d-spels are collected from each image sensor and from each image sensor position;
• the points are triangulated and texture mapped to form the photo-realistic models.
• This model is appropriate for modeling objects that are in the middle of or surrounded by image sensors located at various positions. It is an improvement over the single image sensor method since it allows the possibility of collecting additional 3d-spels to model the top or bottom of an object.
• an exemplary Pan and Tilt Imager 202 is built by placing an image sensor 198 on a platform 204 that can pan (rotate in the horizontal plane about the y-axis) and/or tilt (rotate up and down in the vertical plane about the x-axis).
• the initial position of the modeler is not important, however it is generally considered as being rotated by zero degrees on each axis.
• pan and tilt imager method distinguishes itself from the other two methods in several ways:
• the focal point of the image sensor lens is considered to be the origin of the scene being modeled.
• the system is more appropriate for modeling a scene, for example the interior of a room or the interior of some other enclosed space.
• the user specifies which subset of the space is going to be mapped. Due to the varying distances to the boundaries of the space, the field of view may change. For example, if the image sensor is in its initial position, the distance to the enclosure boundaries and the size of the image sensor lens will determine the field of view. If the image sensor is then tilted up or down, or rotated left or right, the distance to the boundary may change. If this distance increases, the field of view becomes larger. If the distance decreases, the field of view becomes smaller.
• the DLSS system automatically determines a sequence of pans and tilts that will cover the entire area to be modeled.
• the sequence that generates the model is given here:
• the user selects the region to be modeled from a live view or previously taken digital images.
• 3d-spels are collected by the previously described methods.
• Triangulation and Texture mapping generate the final 3d, photo-realistic model as shown in Figure 21.
• Figure 25 shows an alternate imager 206 for the imager 198 of Figure 22.
• the alternate image 206 has an array of fixed sensors 208 that replace the movable camera 197 and the slider 196.
• the fixed sensors 208 can advantageously be configured to take a plurality of images at fixed offsets simultaneously, without any delay as incurred by having a camera moving on a slider base between successive images.
• the distance between the end sensors of the array of fixed sensors corresponds to the length of the slider base 196 of the sensor having a movable camera.
• the preferred DLSS embodiments model objects or scenes (spaces) and generate spatially accurate, photo-realistic 3D models of the objects or spaces. It is thus advantageous to have a viewing tool for examining and manipulating the generated 3D models.
• Figure 26 shows an exemplary viewing and imaging tool window 210 suitable for use with DLSS 3D models.
• the viewing and imaging tool displays the generated 3D point cloud data or 3D textured models 212 while, at the same time, providing navigation tools that enable viewing the models at every possible three-dimensional angle.
• the tool can be used for inspecting, measuring and ensuring the quality of textured 3D models before exporting the models into other applications.
• the viewing tool has file menu selections 214 for opening 3D models, opening 3D point clouds, importing other 3D model types, exporting to other 3D model types, and exiting the viewer.
• Edit menu selections 216 are provided for copying selected sets of points, cutting selected sets of points, pasting sets of points from previous copy or cut operations, and for deleting sets of points.
• a selection is also provided for setting user preferences.
• a view menu 218 provides selections for setting a navigation mode, for adjusting the field of view, for centering the viewed model in the view area, and for selecting various viewpoints.
• Optional tool bars 220 and status bars 222 are also provided.
• the exemplary viewing and imaging tool provides four ways to view 3D models.
• a fly mode provides flexible navigation in the view area. This mode is similar to the interactive modes used on many interactive video game systems.
• a spin mode the default mode, permits rotating the 3D model 212 in the view area on each of its axes so the model can be viewed from any angle.
• a pan mode allows the user to pan around the 3D model 212 in the view area.
• a zoom mode provides for zooming in towards the 3D model 212 or out from the 3D model 212. While the aforementioned modes provide the essential requirements for viewing 3D models, the viewing and imaging tool is not limited in scope to these modes and other modes may be provided.

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